G. Bacci, E. V. Belmega, P. Mertikopoulos, and L. Sanguinetti. IEEE Transactions on Wireless Communications, vol. 14, no. 9, pp. 4728–4742, September 2015.

This work proposes a distributed power allocation scheme for maximizing energy efficiency in the uplink of orthogonal frequency-division multiple access (OFDMA)-based heterogeneous networks (HetNets). The user equipment (UE) in the network are modeled as rational agents that engage in a non-cooperative game where each UE allocates its available transmit power over the set of assigned subcarriers so as to maximize its individual utility (defined as the user’s throughput per Watt of transmit power) subject to minimum-rate constraints. In this framework, the relevant solution concept is that of Debreu equilibrium, a generalization of Nash equilibrium which accounts for the case where an agent’s set of possible actions depends on the actions of its opponents. Since the problem at hand might not be feasible, Debreu equilibria do not always exist. However, using techniques from fractional programming, we provide a characterization of equilibrial power allocation profiles when they do exist. In particular, Debreu equilibria are found to be the fixed points of a water-filling best response operator whose water level is a function of minimum rate constraints and circuit power. Moreover, we also describe a set of sufficient conditions for the existence and uniqueness of Debreu equilibria exploiting the contraction properties of the best response operator. This analysis provides the necessary tools to derive a power allocation scheme that steers the network to equilibrium in an iterative and distributed manner without the need for any centralized processing. Numerical simulations are then used to validate the analysis and assess the performance of the proposed algorithm as a function of the system parameters, also discussing key design tradeoffs to meet 5G requirements (e.g., obtaining more than $500 b/s/Hz/km^2$ area spectral efficiency) with a reasonable amount of physical resources (e.g., bandwidth and transmit power), and of complexity at the receiving stations, such as minimal information requirements at the user level and number of antennas.

arXiv link: https://arxiv.org/abs/1408.7004